Jug: A Task-Based Parallelization Framework

What is Jug?

Jug allows you to write code that is broken up into tasks and run different tasks on different processors.

It currently has two backends. The first uses the filesystem to communicate between processes and works correctly over NFS, so you can coordinate processes on different machines. The second is based on redis so the processes only need the capability to connect to a common redis server.

Jug also takes care of saving all the intermediate results to the backend in a way that allows them to be retrieved later.


Short Example

Here is a one minute example. Save the following to a file called primes.py:

from jug import TaskGenerator
from time import sleep

def is_prime(n):
    for j in xrange(2,n-1):
        if (n % j) == 0:
            return False
    return True

primes100 = map(is_prime, xrange(2,101))

Of course, this is only for didactical purposes, normally you would use a better method. Similarly, the sleep function is so that it does not run too fast.

Now type jug status primes.py to get:

Task name                     Waiting       Ready    Finished     Running
primes.is_prime                     0          99           0           0
Total:                              0          99           0           0

This tells you that you have 99 tasks called primes.is_prime ready to run. So run jug execute primes.py &. You can even run multiple instances in the background (if you have multiple cores, for example). After starting 4 instances and waiting a few seconds, you can check the status again (with jug status primes.py):

Task name                     Waiting       Ready    Finished     Running
primes.is_prime                     0          63          32           4
Total:                              0          63          32           4

Now you have 32 tasks finished, 4 running, and 63 still ready. Eventually, they will all finish and you can inspect the results with jug shell primes.py. This will give you an ipython shell. The primes100 variable is available, but it is an ugly list of jug.Task objects. To get the actual value, you call the value function:

In [1]: primes100 = value(primes100)

In [2]: primes100[:10]
Out[2]: [True, True, False, True, False, True, False, False, False, True]

More Examples

There is a worked out example in the tutorial, and another, fully functioning in the examples/ directory.

How do I get Jug?

You can either get the git repository at


Or download the package from PyPI. You can use easy_instal jug or pip install jug if you’d like.

What’s New

version 0.9.4 (Apr 15 2013) - Add CustomHash wrapper to set __jug_hash__ - Print traceback on import error - Exit when no progress is made even with barrier - Use Tasklets for better jug.mapreduce.map - Use Ipython debugger if available (patch by Alex Ford) - Faster –aggressive-unload - Add currymap() function

version 0.9.3 (Dec 2 2012) - Fix parsing of ports on redis URL (patch by Alcides Viamontes) - Make hashing robust to different orders when using randomized hashing

(patch by Alcides Viamontes)
  • Allow regex in invalidate command (patch by Alcides Viamontes)
  • Add --cache --clear suboption to status
  • Allow builtin functions for tasks
  • Fix status –cache`` (a general bug which seems to be triggered mainly by bvalue() usage).
  • Fix CompoundTask (broken by earlier __jug_hash__ hook introduction)
  • Make Tasklets more flexible by allowing slicing with Tasks (previously, slicing with tasks was not allowed)

version 0.9.2 (Nov 4 2012): - More flexible mapreduce()/map() functions - Make TaskGenerator pickle()able and hash()able - Add invalidate() method to Task - Add –keep-going option to execute - Better help messsage

version 0.9.1 (Jun 11 2012): - Add –locks-only option to cleanup subcommand - Make cache file (for status subcommand) configurable - Add webstatus subcommand - Add bvalue() function - Fix bug in shell subcommand (value was not in global namespace) - Improve identity() - Fix bug in using Tasklets and –aggressive-unload - Fix bug with Tasklets and sleep-until/check

version 0.9: - In the presence of a barrier(), rerun the jugfile. This makes barrier much

easier to use.
  • Add set_jugdir to public API
  • Added CompoundTaskGenerator
  • Support subclassing of Task
  • Avoid creating directories in file backend unless it is necessary
  • Add jug.mapreduce.reduce (which mimicks the builtin reduce)

See the file ChangeLog for the full history

What do I need to run Jug?

It is a Python only package. I have tested it with Python 2.5 and 2.6. I do not expect Python 2.4 or earlier to work (this is not a priority). Python 3.0 will not work either (this is expected to change in the future—patches are welcome).

How does it work?

Read the tutorial.

What’s the status of the project?

Beta (or thereabouts).

This is still in development and APIs are not fixed, but are in less flux than they were earlier in the project and it is very usable.

It is usable, though. I have used it for my academic projects for the past two years and wouldn’t now start any other project without using jug. It’s become a major part of the way I handle projects with a large number of computations and cluster usage.

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